Clinical hypertension · 2025
Nutriomics and artificial intelligence nutrition obesity cohort (NAINOC): a design paper for a prospective cohort for nutrition and obesity research.
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Lee Minyoung, Park Sungha, Park Soo-Hyun, Park Ho-Young, Lee Yu Ra, Kim Min-Sun, Nam Miso, Lee Jangho, Seo Hyein, Lee Yong-Ho, Lee Chan Joo, Park Jae-Ho, Yoo Hye Hyun, Kim Hyun-Jin, Shin Kyong-Oh, Uchida Yoshikazu, Park Kyungho
Аннотация
BACKGROUND: The increase in obesity is becoming a world-wide health issue. However, no prospective cohorts in East Asia have thoroughly explored comprehensive nutritional and multiomic data in individuals with obesity. This study is designed to establish an obesity cohort that constitutes clinical characteristics, nutritional status, laboratory profiles, metabolic complication studies, and multiomic profiles with the goal of artificial intelligence platform-based nutriomic analysis.
METHODS: This study aims to enroll at least 400 obese adults (aged ≥ 19 years; body mass index ≥ 25 kg/m2) and 100 non-obese adults as controls. Obese participants have to have at least one of the following chronic metabolic diseases: hypertension, type 2 diabetes mellitus, cardiovascular disease, and metabolic syndrome. Participants will undergo assessment for demographic data, clinical, lifestyle, and dietary assessments, laboratory examination, coronary calcium/visceral fat scan, liver fibroscan, carotid ultrasound, and continuous glucose monitoring. Metabolite analysis will be conducted for blood/stool/urine/saliva samples. Deoxyribonucleic acid methylation analysis, peptidomic analysis, and lipidomic analysis will be performed on blood samples. Obese individuals will have annual study visits for collection of clinical measures and multiomics data over a 5-year period. Control individuals will have a baseline hospital visit with annual telephone follow-up for clinical event monitoring.
CONCLUSIONS: The strength of this cohort will be as follows. First, the cohort will enable the integration of nutritional intake data with other multiomics data for a comprehensive analysis. Second, inclusion of both obese individuals with various metabolic traits and non-obese individuals as controls is advantageous for studying a wide range of obesity phenotypes in comparison with non-obese conditions. Third, diverse modalities to assess metabolic and complication status will facilitate multifaceted analysis. Lastly, beyond the typical blood and stool samples in multiomic studies, the inclusion of urine, saliva, and skin samples will further refine obesity characterization.
METHODS: This study aims to enroll at least 400 obese adults (aged ≥ 19 years; body mass index ≥ 25 kg/m2) and 100 non-obese adults as controls. Obese participants have to have at least one of the following chronic metabolic diseases: hypertension, type 2 diabetes mellitus, cardiovascular disease, and metabolic syndrome. Participants will undergo assessment for demographic data, clinical, lifestyle, and dietary assessments, laboratory examination, coronary calcium/visceral fat scan, liver fibroscan, carotid ultrasound, and continuous glucose monitoring. Metabolite analysis will be conducted for blood/stool/urine/saliva samples. Deoxyribonucleic acid methylation analysis, peptidomic analysis, and lipidomic analysis will be performed on blood samples. Obese individuals will have annual study visits for collection of clinical measures and multiomics data over a 5-year period. Control individuals will have a baseline hospital visit with annual telephone follow-up for clinical event monitoring.
CONCLUSIONS: The strength of this cohort will be as follows. First, the cohort will enable the integration of nutritional intake data with other multiomics data for a comprehensive analysis. Second, inclusion of both obese individuals with various metabolic traits and non-obese individuals as controls is advantageous for studying a wide range of obesity phenotypes in comparison with non-obese conditions. Third, diverse modalities to assess metabolic and complication status will facilitate multifaceted analysis. Lastly, beyond the typical blood and stool samples in multiomic studies, the inclusion of urine, saliva, and skin samples will further refine obesity characterization.